class DeploymentSystem:
    def __init__(self, pipeline, monitoring_interval=3600):
        self.pipeline = pipeline
        self.monitoring_interval = monitoring_interval
        self.performance_history = []
        
    def deploy_model(self, model_path):
        """部署模型"""
        # 加载模型
        self.model = tf.keras.models.load_model(model_path)
        print(f"Model deployed from {model_path}")
        
    def monitor_performance(self, live_data):
        """监控模型性能"""
        current_metrics = self._evaluate_on_live_data(live_data)
        self.performance_history.append({
            'timestamp': pd.Timestamp.now(),
            'metrics': current_metrics
        })
        
        # 检查性能下降
        if self._check_performance_degradation():
            print("Warning: Model performance degradation detected!")
            self._trigger_retraining()
    
    def _evaluate_on_live_data(self, live_data):
        """在实时数据上评估"""
        # 实现评估逻辑
        return {'accuracy': 0.95, 'precision': 0.93, 'recall': 0.94}
    
    def _check_performance_degradation(self):
        """检查性能下降"""
        if len(self.performance_history) < 2:
            return False
        
        recent_acc = self.performance_history[-1]['metrics']['accuracy']
        previous_acc = self.performance_history[-2]['metrics']['accuracy']
        
        return recent_acc < previous_acc - 0.05  # 准确率下降超过5%
    
    def _trigger_retraining(self):
        """触发重新训练"""
        print("Initiating model retraining...")
        # 实现重新训练逻辑
        
    def generate_monitoring_report(self):
        """生成监控报告"""
        report_data = []
        for record in self.performance_history:
            report_data.append({
                'timestamp': record['timestamp'],
                **record['metrics']
            })
        
        report_df = pd.DataFrame(report_data)
        
        # 可视化监控结果
        plt.figure(figsize=(12, 8))
        for metric in ['accuracy', 'precision', 'recall']:
            plt.plot(report_df['timestamp'], report_df[metric], label=metric, marker='o')
        
        plt.title('Model Performance Monitoring')
        plt.xlabel('Time')
        plt.ylabel('Score')
        plt.legend()
        plt.xticks(rotation=45)
        plt.tight_layout()
        plt.show()
        
        return report_df

# 部署示例
deployment = DeploymentSystem(pipeline)
deployment.deploy_model('best_model.h5')

# 模拟监控
for i in range(10):
    live_data = np.random.randn(100, 30, 1)  # 模拟实时数据
    deployment.monitor_performance(live_data)
    time.sleep(1)  # 模拟时间间隔

# 生成监控报告
monitoring_report = deployment.generate_monitoring_report()
